Introduction
Named Entity Recognition is an essential concept in natural language processing. If we want to develop any Natural Language Processing application, we cannot produce it without named entity recognition. The basic definition of named entity recognition detects named entities like Person, organization, place date, etc. For example, in the line- John lives in America and has worked for Microsoft since April 2020; the named entities are
1.John is a person
2.America is a place
3.April 2020 is a date
4.Microsoft is an organisation
You should have a question: why should we detect the named entities for natural language processing? In natural language processing, the first step is Tokenization. We need to tokenize a sentence into additional granule tokens, and then we need to determine the entity types for these tokens and samples. Named entity recognition as supervised machine learning, we need to teach the model based on the training data and labels. We use validation data for tuning the hyperparameters. When the model is trained, we feed the unknown test data and check the accuracy metric of our model.
When it comes to humans, the category definition is intuitively clear. However, the classification can be a bit ambiguous when it comes to computers.
Methods of Named Entity Recognition
- We can use different machine learning algorithms to train the model for multiclass classification is one of the ways, but it requires a lot of labelling. The model also requires a deep understanding of the context to deal with ambiguity in sentences. So, the task is quite challenging for simple machine learning.
- NLTK and NLP's Speech tiger implement conditional random fields that allow terms to be modeled probabilistically. CRF Captures the context of the message very well.
- Deep learning uses a technique known as word embedding that can understand the syntactic relationship between words. The Deep Learning NER is much more accurate than previous methods. The reason for this is that deep learning NER assembles words much more accurately than the previous methods.
- In addition, Deep learning NERs Can also be trained to analyze high-level terminology and topic-specific terms. This makes them suitable for use in several situations.
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